Traditional dimensionality reduction techniques include
Principal Component Analysis (PCA), Independent Component Analysis (ICA), and Multidimensional Scaling (MDS). PCA is one of the most popular linear reduction technique and dates back to Karl Pearson in 1901 [1]}. This technique tries to find orthogonal directions explaining as much variance of data as possible [2]}. [3]} applied PCA to evaluate energy security of multiple East Asian countries with respect to vulnerability, efficiency, and sustainability. [4]} proposed a network anomaly detection method that first reduced dimensionality by combining information gain and PCA and fed the extracted features into an ensemble classifier based on support vector machine (SVM), instance-based learning algorithms, and multilayer perceptron for the detection. Due to its attractive advantages of minimal information loss and generation of uncorrelated dimensions, PCA is still popular for use in a broad range of application areas [5]}. ICA, on the other hand, tries to extract independent pieces of information from high-dimensional data mainly for the purpose of source blind separation, which has been popular in signal processing [6]}, [7]}. ICA can be useful in other areas of study. For example, [8]} discussed the benefits of ICA to unravel the complexity of cancer biology, particularly in analyzing different types of omics datasets. Different from PCA and ICA, MDS is a nonlinear dimensionality reduction technique. It provides useful graphical representation of data based on similarity information of individual data points. MDS is a common technique for analyzing network-structured data as shown in [9]} that applied MDS to wireless networks localization.
